llnl / llnl/mada-tools

Benchmarking Tests: Benchmarks to Test

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#42 0 comments 0 reactions 2 assignees View on GitHub

@ndmasters is already working on this.

Since Aug 5, 2026.

enhancement
Dominant language
Python
Stars
4
Forks
1
Avg merge
2d 5h
Merged PRs (30d)
3

Description

There are many benchmarks to test. Decide on the minimum and expand from there. These are some idea below.

  • Paper from https://github.com/Accenture/mcp-benchis located here https://arxiv.org/abs/2508.20453
    • Maybe we can use this but haven’t looked too much into it
    • See what the connections are since we already connect to the models
  • Types of failures
    • Called the right tool but with wrong parameters (parameters that don’t exist in the method)
    • Didn’t call the right tool but provided correct parameters
    • Called the right tool but interpreted parameters wrong (parameters extracted from sentence incorrectly e.g. passed 10 runs to lower bounds)
    • Prompt didn’t provide all the necessary parameters
    • Prompt provided more than required parameters
    • Non-valid inputs e.g. only positives but gave it a negative
      • Fix in mcp server to capture these out of bounds
      • Update docstrings
      • Looking for paths or files and they got updated
    • Machine is down, resources aren't available
    • Time out errors
      • Model waiting for user input e.g. some string and then enter
      • Create simple simulations to run faster
      • Caught at the ci/cd time limit exceeded
      • Gitlab duo
      • Running simulation that takes longer than usual
Field Why
task_id Ties to a specific scenario
tools_called Trajectory for debugging ß we probably want to track the order here and maybe args provided as well
task_success Binary pass/fail
llm_judge_score Quality of final output (1-5)
total_tool_calls Efficiency
total_latency_ms Performance
model_version Catch regressions across model updates
timestamp Trend analysis over time

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